Green's Function Zeros in Fermi Surface Symmetric Mass Generation

The Fermi surface symmetric mass generation (SMG) is an intrinsically interaction-driven mechanism that opens an excitation gap on the Fermi surface without invoking symmetry-breaking or topological order. We explore this phenomenon within a bilayer square lattice model of spin-1/2 fermions, where the system can be tuned from a metallic Fermi liquid phase to a strongly-interacting SMG insulator phase by an inter-layer spin-spin interaction. The SMG insulator preserves all symmetries and has no mean-field interpretation at the single-particle level. It is characterized by zeros in the fermion Green's function, which encapsulate the same Fermi volume in momentum space as the original Fermi surface, a feature mandated by the Luttinger theorem. Utilizing both numerical and field-theoretical methods, we provide compelling evidence for these Green's function zeros across both strong and weak coupling regimes of the SMG phase. Our findings highlight the robustness of the zero Fermi surface, which offers promising avenues for experimental identification of SMG insulators through spectroscopy experiments despite potential spectral broadening from noise or dissipation.Comment: 12 pages, 7 figures. 1 appendi

Definition and Classification of Fermi Surface Anomalies

We propose that the Fermi surface anomaly of symmetry group $G$ in any dimension is universally classified by $G$-symmetric interacting fermionic symmetry-protected topological (SPT) phases in $(0+1)$-dimensional spacetime. The argument is based on the perspective that the gapless fermions on the Fermi surface can be viewed as the topological boundary modes of Chern insulators in the phase space (position-momentum space). Given the non-commutative nature of the phase space coordinates, we show that the momentum space dimensions should be counted as negative dimensions for SPT classification purposes. Therefore, the classification of phase-space Chern insulators (or, more generally fermionic SPT phases) always reduces to a $(0+1)$-dimensional problem, which can then be answered by the cobordism approach. In addition to the codimension-1 Fermi surface case, we also discuss the codimension-$p$ Fermi surface case briefly. We provide concrete examples to demonstrate the validity of our classification scheme, and make connections to the recent development of Fermi surface symmetric mass generation.Comment: 13 pages + references, 2 figures, 2 tables. Update Tab. II, clarifications to codimension-p Fermi surface, and references adde

Emergent self-duality in long range critical spin chain: from deconfined criticality to first order transition

Over the past few decades, tremendous efforts have been devoted to understanding self-duality at the quantum critical point, which enlarges the global symmetry and constrains the dynamics. In this letter, we employ large-scale density matrix renormalization group simulations to investigate the critical spin chain with long-range interaction $V(r) \sim 1/r^{\alpha}$. Remarkably, we reveal that the long-range interaction drives the deconfined criticality towards a first-order phase transition as $\alpha$ decreases. More strikingly, the emergent self-duality leads to an emergent symmetry and manifests at these first-order critical points. This discovery is reminiscent of self-duality protected multicritical points and provides the example of the critical line with generalized symmetry. Our work has far-reaching implications for ongoing experimental efforts in Rydberg atom quantum simulators.Comment: 5 + 10 pages, 9 figures. Any comments or suggestions are welcome

Superconductivity from Doping Symmetric Mass Generation Insulators: Application to La3_3Ni2_2O7_7 under Pressure

We investigate the bilayer nickelates as a platform to realize the symmetric mass generation (SMG) insulator, a featureless Mott insulator that arises due to the Lieb-Schultz-Mattis (LSM) anomaly cancellation in bilayer spin-1/2 lattice systems. Through a single-orbital bilayer square lattice model involving intralayer hopping $t$ and interlayer superexchange interaction $J$, we demonstrate the emergence of high-temperature superconductivity (SC) upon doping the SMG insulator. The SC phase features $s$-wave interlayer spin-singlet pairing and exhibits a crossover between the BCS and BEC limits by tuning the $J/t$ ratio. We estimate the SC transition temperature $T_c$ from both the weak and strong coupling limits at the mean-field level. Our findings offer insights into the experimentally observed decrease in $T_c$ with pressure and the strange metal behavior above $T_c$. Additionally, we propose that both Ni $3d_{z^2}$ and $3d_{x^2-y^2}$ orbitals can exhibit superconductivity in La$_3$Ni$_2$O$_7$ under pressure, but their $T_c$ should vary in opposite ways under doping. This characteristic difference suggests a potential experimental pathway to identify which electronic orbital plays the principal role in the formation of superconductivity in this system.Comment: 11 pages, 5 figures, 2 table

Streaming CTR Prediction: Rethinking Recommendation Task for Real-World Streaming Data

The Click-Through Rate (CTR) prediction task is critical in industrial recommender systems, where models are usually deployed on dynamic streaming data in practical applications. Such streaming data in real-world recommender systems face many challenges, such as distribution shift, temporal non-stationarity, and systematic biases, which bring difficulties to the training and utilizing of recommendation models. However, most existing studies approach the CTR prediction as a classification task on static datasets, assuming that the train and test sets are independent and identically distributed (a.k.a, i.i.d. assumption). To bridge this gap, we formulate the CTR prediction problem in streaming scenarios as a Streaming CTR Prediction task. Accordingly, we propose dedicated benchmark settings and metrics to evaluate and analyze the performance of the models in streaming data. To better understand the differences compared to traditional CTR prediction tasks, we delve into the factors that may affect the model performance, such as parameter scale, normalization, regularization, etc. The results reveal the existence of the ''streaming learning dilemma'', whereby the same factor may have different effects on model performance in the static and streaming scenarios. Based on the findings, we propose two simple but inspiring methods (i.e., tuning key parameters and exemplar replay) that significantly improve the effectiveness of the CTR models in the new streaming scenario. We hope our work will inspire further research on streaming CTR prediction and help improve the robustness and adaptability of recommender systems